PROJECT SUMMARY This proposal responds to PAR-12-198 (Improving Diet and Physical Activity Assessment). It will develop and test novel multilevel statistical methods to examine the effects of subject-level parameters (variance and slope) of time-varying variables in ecological momentary assessment (EMA) studies of physical activity. Low level of physical activity heightens the risk of numerous deadly diseases (e.g., heart disease, stroke, cancer, diabetes) throughout the life course. The use of EMA in physical activity research is growing rapidly because real-time data capture methods supply novel insights into determinants of this behavior. In EMA studies, it is common to have up to thirty or forty observations per subject, and this allows us to model subject-level parameters such as variances (e.g., how erratic is a subject's mood?) and slopes (e.g., how much does a subject's mood change across contexts?) of time-varying variables. For example, in our recent EMA work, we have found that more physically active children have greater positive and negative emotional stability than children who are less physically active. However, current multilevel modeling strategies are restricted to treating subject-level variances and slopes as outcomes. As a consequence, statistical models do not have the ability to test whether subject-level variance and slope parameters have predictive, mediating, and moderating effects on physical and sedentary activity. For example, we are unable to ask important research questions such as whether erratic mood mediates the effects of depression on physical activity, or whether the effects of living in a highly walkable neighborhood on physical activity are attenuated for individuals with unstable self- efficacy beliefs. This modeling restriction severely limits our ability to capitalize on the full potential of the time- varying nature of EMA data to enhance physical activity research. To address this critical methodological gap, we propose to develop multilevel models, software, and strategies to test for the effects of these parameters in EMA studies. We will apply these modeling strategies to secondary analyses of pooled data from five federally- and foundation-supported EMA studies of physical activity with a combined sample size of N = 553 participants (including children and adults). The primary aims are (1) to develop novel multilevel modeling strategies and software to test whether subject-level variance and slope parameters have predictive, mediating, and moderating effects on subject-level physical and sedentary activity outcomes and (2) to apply these novel modeling strategies and software in secondary analyses of existing EMA datasets to examine the effects of subject-level variance and slopes of time-varying variables such as safety, stress, fatigue, and self-efficacy on physical and sedentary activity. This study has the potential to make novel methodological and substantive contributions for analysis of EMA data in physical activity research. The methods to be developed can easily generalize to a variety of chronic disease-relevant research areas.